Large datasets with large numbers of associated categories are difficult to navigate quickly. In some cases, filtering on certain categories will only eliminate one or two records from the dataset. Prior art techniques generally provide a list of categories and attributes to filter on without indicating or determining how the filters will affect the resulting dataset. In many cases, the prior art provides a pre-determined hierarchy of categories to which records are indexed.
In view of the foregoing, it would be highly desirable to provide enhanced techniques for determining which categories will filter data efficiently.
Many users have difficulty analyzing datasets. While some users are of various components in a dataset, they commonly do not have the tools or skills to locate the components. Performing an analysis includes the selection of components, such as categories (also known as dimensions), measures and the like. Efficient navigation of a dataset often relies on a user's knowledge of the structure of the dataset. Knowledge of the dataset's structure fosters selection of filters to partition the dataset and select measures. This knowledge and corresponding analysis skills are outside the skill set of a vast number of computer users.
In view of the foregoing it would be desirable to provide enhanced techniques for navigating datasets. It would be highly desirable to provide enhanced techniques for selecting measures, selecting categories and applying filters as part of performing analyses on a dataset.